Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are prone to insulation failure from ageing, which has fatal and economic consequences if left undetected or unattended. Traditional detection methods are based on scheduled maintenance practices that often involve taking samples from in situ transformers and analysing them in laboratories using several techniques. This conventional method exposes the engineer performing the test to hazards, requires specialised training, and does not guarantee reliable results because samples can be contaminated during collection and transportation. This paper reviews the transformer oil types and some traditional ageing detection methods, including breakdown voltage (BDV), spectroscopy, dissolved gas analysis, total acid number, interfacial tension, and corresponding regulating standards. In addition, a review of sensors, technologies to improve the reliability of online ageing detection, and related online transformer ageing systems is covered in this work. A non-destructive online ageing detection method for in situ transformer oil is a better alternative to the traditional offline detection method. Moreover, when combined with the Internet of Things (IoT) and artificial intelligence, a prescriptive maintenance solution emerges, offering more advantages and robustness than offline preventive maintenance approaches.

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Modern society continues to depend heavily on the wealth and services generated by highly automated and mechanised systems. Therefore, physical assets, including manual and automated systems, must be directed and safeguarded. When these assets fail, life, property, credibility, and trust are typically lost. In the face of rapidly changing technology and maintenance philosophies, one way to avoid failures and catastrophes is to learn from past failures and implement the lessons or recommendations derived from such analyses and reports. Nigeria, like other nations, has experienced disasters caused by asset failures in businesses, factories, and residences.Consequently, the purpose of this article is to raise public awareness and alarm regarding the repercussions of the Nigerian aviation industrys failure to learn from past failures and disasters. As a case study, it employs a documented Dana Air aircraft accident report. In addition, it employs Fault Tree Analysis (FTA), a deductive failure technique, and Reliability Block Diagram (RBD) to gain a deeper understanding of the aircraft accident and its root causes. As a result, a feedback loop is established for the development of additional ideas and actions to prevent future disasters in Nigeria.

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